JMIR Medical Education

Teaching severe pelvic trauma poses a significant challenge in orthopedic surgery education due to the necessity of both clinical reasoning and procedural operational skills for mastery. Traditional methods of instruction, including theoretical teaching and mannequin practice, face limitations due to the complexity, the unpredictability of treatment scenarios, the scarcity of typical cases, and the abstract nature of traditional teaching, all of which impede students’ knowledge acquisition.

Medical education has not traditionally recognised patient safety as a core subject. To foster a culture of patient safety and enhance psychological safety, it is essential to address the barriers and facilitators that currently impact the development and delivery of medical education curricula. The aim of including patient safety and psychological safety competencies in education curricula is to hardwire these into the genome of the modern healthcare worker.

Open AI released version ChatGPT3.5 (Chat Generative Pre-Trained Transformer) and GPT-4 between 2022 and 2023. GPT3.5 has demonstrated proficiency in various examinations, especially in the United States Medical Licensing Examination. However, GPT-4 is even more advanced.

Artificial Intelligence advancements have enabled Large Language Models to significantly impact radiology education and diagnostic accuracy.

Shared decision making (SDM) is a crucial aspect of patient-centered care (PCC). While several SDM training programs for health care professional have been developed, evaluation of their effectiveness is scarce, especially in mental health disorders like generalized anxiety disorder (GAD).

Learning Health Systems (LHS) have the potential to utilise health data in real-time through rapid and continuous cycles of data interrogation, implementing insights to practice, feedback, and practice change. However, there is a lack of an appropriately skilled interprofessional informatics workforce that can leverage knowledge to design innovative solutions. Therefore, there is a need to develop tailored professional development training in digital health, to foster skilled interprofessional learning communities in the healthcare workforce in Australia.

Artificial intelligence (AI) is widely applied across several industries, including medical education. The content validation and its answers are based on training datasets and the optimization of each model. The accuracy of large language models AI in basic medical examinations and the factors related to its accuracy have been explored.

Artificial intelligence (AI) is set to shape the future of medical practice. The perspective and understanding of medical students are critical for guiding the development of educational curricula and training.

Future health professionals, including dentists, must critically engage with digital health technologies to enhance patient care. Whilst digital health is increasingly being integrated into health professions curricula, its interpretation varies widely depending on discipline, healthcare setting, and local factors. This viewpoint proposes a structured set of domains to guide the design of a digital health curriculum tailored to the unique needs of dentistry in Australia. The paper aims to share a premise for curriculum development that aligns with current evidence and national digital health strategy, serving as a foundation for further discussion and implementation in dental programs.

It is well established that frontline healthcare staff are particularly at risk of stress. Resilience is important to help staff to manage daily challenges and to protect against burnout.

Medical students often struggle to engage with and retain complex pharmacology topics during their preclinical education. Traditional teaching methods can lead to passive learning and poor long-term retention of critical concepts.

Recent advancements in AI technology have begun to play a crucial role in medical education. AI models, such as ChatGPT, have shown promise in various applications, including answering medical questions and assisting in clinical decision-making. However, there is limited research on the performance of these models on comprehensive medical licensing examinations.